- Email: [email protected]
Enterprise modeling and integration: A stochastic management approach
ARTICLE IN PRESS Int. J. Production Economics 122 (2009) 485–491
Contents lists available at ScienceDirect
Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe
Enterprise modeling and integration: A stochastic management approach Masayuki Matsui Department of Systems Engineering, The University of Electro-Communications, Chofu-Shi, Tokyo 182-8585 Japan
a r t i c l e in fo
abstract
Available online 21 June 2009
Recently, my book by Matsui has been published in Japanese, and the title is ‘‘Management of Manufacturing Enterprise: Profit Maximization and Factory Science.’’ Enterprise/Factory Science means the system science of the 3M & I, in which is the complex of huMan, Material/Machine, Money and Information, and would contribute to enterprise modeling and integration issues. This paper presents the problem of enterprise/factory science, and discusses a class of 3M & I system by a stochastic management approach. First, the 3M & I system is defined, and a variety vs. structure is introduced. Next, the two structures of management are distinguished, and the demand-to-supply and process-cycle management are discussed by structural modeling. & 2009 Elsevier B.V. All rights reserved.
Keywords: 3M & I system Enterprise modeling Demand-to-supply Process cycle Ellipse/switching
1. Introduction Taylor, the father of scientific management, said in 1903 that management is an art (Taylor, 1903). By ‘‘an art’’ he meant the scientific law/technique (enterprise/factory) and the applicative ‘‘waza’’ (in Japanese, skill/know-how). Following that, this definition was further developed by Koontz and O’Donnell (1959) and others. About 100 years of the history of management has passed since Taylor established a theory of scientific management (Taylor, 1903). However, management theory regarding what management actually still seems to be confusing (Koontz and O’Donnell, 1959) and at a static/ statistical level when referring to history of management. The statistical approach was effective in the era of mass production since scientific management. However, this approach would be limited to the class of average management and not proper to develop the micro I systematic management in the era of individual. This chapter is preparation for developing management theories through modeling experiments by a
E-mail address: [email protected] 0925-5273/$ - see front matter & 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2009.06.023
stochastic approach. Towards this purpose, we have developed a system approach to management modeling, and attempt to build a framework for the management research in the future.
2. Stochastic management 2.1. 3M & I system As management is always facing environmental changes, it is natural to consider stochastic management by stochastic approaches (Buzacott and Shanthikumar, 1963; Crabill et al., 1977; Hopp and Spearman, 2000; Matsui, 1993). In stochastic management, the structure design theory is important for dispersing risk and for buffer design. At the beginning, we present general management problems for a variety of 3M & I system. Next, we consider the structure design problem for the general management problem and present a new or unified approach for the art of variety. The system is usually defined as the 3M & I system, consisting of huMan, Material/Machine, Money and
ARTICLE IN PRESS 486
M. Matsui / Int. J. Production Economics 122 (2009) 485–491
Information as shown in Fig. 1. The use of 3M represents resources, while I represent methods. The possible states of the 3M & I system are usually numerous, and the state space of variety is our main concern. Then, the management is here regarded as the management of the 3M & I system as shown in Fig. 2. The 3M & I manufacturing/enterprise is further divided into project and repetitive types, and the latter type is our object system and is treated below. Without the loss of generality, the 3M system is assumed to vary stochastically in time processes under risks.
Environment Material Machine
Man
&
Money
Information
Fig. 1. 3M & I system.
(man nnel
3. Demand-to-supply management
)
Perso
ney
(mo
Manufacture (mertial/machines)
The management is now defined as an art of variety in a 3M & I system under the goal, and is called the general management problem. Individualization and uniformity are the opposite extremes of variety, and usual management (standardization) occurs between the extremes. There is a law of requisite variety (Ashby, 1956). If the redundancy (Shannon and Weaver, 1964) in information theory is introduced as the measure of management constraints, the degree of management standardization/ structure can be shown. Recent globalization in the world is moving towards standardization, while manufacturing culture is moving toward individualization. Since there are various kinds of management which are diversified, management also has a redundancy of structures for standardizations. According to past literature, the management process and demand-to-supply structures would be the two main types of management structures. Each structure plays a vital role in preventing variety through the I (information/method) from disturbance. In our examination, we focus on the structures as equilibrium system, and a significant amount of work on management problem is created through stochastic modeling. Since Taylor published ‘‘Shop Management’’ in 1903, about 100 years have passed. With regard to environment/ object, variety and structure, the difference between Taylor’s ideas and today’s ideas are arranged in Table 1, as well as the flow of management principle.
nce
Method
Environment (market, life, nature, etc)
Fina
)
Enterprise
2.2. Variety and structure
3.1. 2-center problem Information/ Quality Marketing (product)
Management
Fig. 2. 3M & I and enterprise.
Recently, there is an increased emphasis on the integration of the separate functional area of the firm. This phenomenon has been reflected in a number of recent textbooks addressing the integration issues between marketing and production management (Eliashberg and Steinberg, 1993). This problem was first pointed out by Follet et al. (1933) (Follet, 1933; (Shapiro, 1977), and it involves a class of heterogeneous agents.
Table 1 100 years since Taylor. Since Taylor
Taylor
Today
Environment/ object
Market/production environment Object system
Economies of scale, division of labor Closed system
Economies of scope job enlargement Economies of agility work cooperation Open system Complex system
Variety
Standardization
Work standardization
Individualization
Human machinery
Standardization of production and quality Human relations job design
Management process
Separation of plan and administration Functionalization (plan)
General management control/ decision CIM flexible
Structure
Demand-to-supply
Globalization ISO standard Multi-agent manufacturing culture Management strategy PDCA/ CAPD ERP/SCM agile/visible
ARTICLE IN PRESS M. Matsui / Int. J. Production Economics 122 (2009) 485–491
487
Table 2 Four types of two centers models. Types
Patterns
Management
Domination
TOC/divisionalization
Sales
Production or Sales
Production
Trade/distributed (non-cooperation)
Compromise
Sales
Production
Integration
Collaboration/centralized (cooperation)
Sales
Production
Sharing
VMI/remote
Sales
Production
Sales
Production
Market risks Customers
Sales (Service) Center
Operational risks Buffer
Production Delivery Center
Lost Customers (Latent demand) Fig. 3. 2-center model.
Now, consider the 2-center model in which enterprises use for sales and production centers (Fig. 3) (Matsui, 1999). The sales center would pursue the maximization of the demand price, while the production center would pursue the minimization of operating cost. For the problem of the two centers, the goal of the factory/enterprise is to maximize the difference between reward and cost under the shorter leadtime by the collaboration/cooperation of the two centers. There are the four types of 2-center models in integration (Table 2). We focus on the stochastic modeling and design for the demand-to-supply management type, and we present a framework theory for management design on the basis of 2-center models (Matsui, 1993, 2006). A general framework for demand-to-supply management model is seen in Fig. 4 and is called the management
game model (MGM) (Matsui, 2002). Based on this framework, some factory systems are distinguished such as stochastic systems as lot/cell production, conveyor systems, job-shop production, flexible manufacturing and so on. 3.2. Toward 3-center In the 3-center model (Yamato et al., 2003), there is a triple relation of 32( ¼ 9) ways. Two major types are centralized and distributed (Fig. 5), and are compared in Table 3 (Matsui, 2006). This difference is similar to that of the enterprise resource planning (ERP) vs. supply chain management (SCM) type, and these would be contrasted to balancing issues. 4. Process-cycle management 4.1. Two problems of cycles The management process is characterized by a management cycle approach, in which functions of management are roughly divided into plan, organization, direction and control (Fayol, 1916). Also, the management cycle is well known as a model of management process structure in industry.
ARTICLE IN PRESS 488
M. Matsui / Int. J. Production Economics 122 (2009) 485–491
Strategic/Gaming Coordination
Sales Center (Pricing Setting/ Order Selection)
Customer/Job
Production Center
Buffer
Delivery
(Cycle/cell, Conveyor, Job-shop, Flexible etc.)
Lost/Subcontract Leadtime Fig. 4. General framework for demand-to-supply.
Sales
Plan(P) Plan(P)
Do(D) Do(D)
Check(C) Check(C)
Check(C) Check(C)
Act(A) Act(A)
Plan(P) Plan(P)
Act(A) Act(A)
S S Sales PP Production
M M
Manufacturing
S S
PP
M M
Do(D) Do(D)
Production
Manufacturing
Fig. 5. Typical types of 3-center: (a) centralized type and (b) distributed type.
Fig. 6. Two cycle model: (a) PDCA type and (b) CAPD type.
material
Table 3 3-center problem: centralized vs. distributed.
X
Comparison
Centralized
Distributed
Relation Division of work Package Module Goal
Star Make ERP Vertical Common
Series Buy SCM Horizontal Individual
transform Information ( order / omote-kanban)
material
X
transform Information ( report / ura-kanban)
As a typical case, the PDCA, Plan-Do-Check-Act, is commonly used in quality control etc. at the factory (Reid and Cseko, 2006; Takahashi, 1999). It may be distinguished as the PDCA cycle, starting from plan, and the CAPD cycle, starting from check, in Fig. 6. For example, the plan corresponds to the setting of the control limit level. The original version of PDCA is seen in the PDS, which consists of Plan (specification/hypothesis), Do (production
Y
Y
Fig. 7. PDCA vs. CAPD: (a) PDCA (feedforward) and (b) CAPD (feedback).
/experiment) and See (inspection/judgment) in statistical quality control (Schewort, 1939). Recently, the CAPD type has been issued.
ARTICLE IN PRESS M. Matsui / Int. J. Production Economics 122 (2009) 485–491
489
Table 4 Classification of management processes. Cycle type
PDCA
CAPD
Type
Design approach Open approach Future planning Goal seeking Top-down
Improvement approach Closed approach Ex post facto action Problem solving Bottom-up
Object
Instruction form Control form Trigger form Retail form Medical-care form
Formal system Feedforward Push system ‘‘goyoukiki’’ Human dock
Informal system Feedback Pull system Supermarket Doctor activity
Technique
Process control Quality control Inventory control Production control Bottleneck control Data control Cost control
Gantt chart (forward) Control chart EOQ MRP MGM (Matsui, 1999) Data mining Standard cost system
Backward Bayesian chart Double-bin JIT TOC/MGM Data analysis Actual cost system
Management
Approach
4.2. PDCA vs. CAPD A comparison of PDCA and CAPD is seen in Fig. 7. The material and information flows are the same as those shown in Fig. 7(a), while these are dual flows as shown in Fig. 7(b). Generally, the difference would be smaller under larger due-time, because the influence of the starting point would be diminished. Also, alternatives of PDCA vs. CAPD are classified in Table 4. An additional feature of PDCA vs. CAPD would be seen in the shorter process cycles with due date barrier, and is treated stochastically as a management cycle model (MCM) with switching (Matsui, 2005).
m Production speed
Demand speed
d)
(ER, EC) Leadtime
Fig. 8. Pair-matrix concept. ER: revenue and EC: operating cost.
tion and minimization. The cross section is the zone of profit maximization. 5. Application case 5.1. Job-shop case The application case of enterprise modeling and integration is the job-shop case of order-selection and switchover, and the lot production case of two service rates and time span. The former is the game-modeling type of two centers, and the latter is the PDCA-modeling type of lot processing. A gaming situation of two centers is treated by the introduction of pair-matrix concept. Fig. 8 illustrates the pair-matrix concept, and Fig. 9 is an application to the enterprise of job-shop type (Matsui and Nakada, 2003). Examples of other types are seen in (Abe et al., 2007; Matsui and Wang, 2007; Yamada et al., 2006). There are the ellipse-cross charts on pair-matrix table. The ellipse-cross chart consists of the ellipses of economics and reliability. The former has the pair poles of revenue maximization (ER*) and cost minimization (EC*). The latter has the pair poles of leadtime maximiza-
5.2. Lot production case A switching situation of PDCA cycle is treated by the introduction of two service rates in lot production. Fig. 10 illustrates the two service rates and penalties, and Fig. 11 is an applied result to the enterprise of lot processing (Matsui, 2005). These are the switching-cross chart on the earliness and tardiness table. The switching-cross chart shows the map of pair (t, k), in which t and k is the processed time and number at switching point. On the lower cost criterion (ECp), there is the no-switching zone in the four corners, and is the switching zone in the central section. 6. Conclusion and future In this paper, the enterprise/factory is regarded as a 3M & I system, and is modeled by a not statistical but stochastic management approach. These include the
ARTICLE IN PRESS 490
M. Matsui / Int. J. Production Economics 122 (2009) 485–491
Maximal revenue
Maximal Leadtime
Maximal profit
Minimal leadtime
Minimal cost
Fig. 9. Pair-matrix table and ellipse (job-shop type). (c1, c2): order-selection criterion (i1, i2): number of backlog (state).
look-ahead cycle
k’ (Switch)
k (Switch)
Workload
look-back cycle
Earliness
Tardiness 2
2
C2
0
1
1
t’
t
C3
T (due time)
Fig. 10. Two service rates and penalties. m1: slower speed and m2: faster speed.
ARTICLE IN PRESS M. Matsui / Int. J. Production Economics 122 (2009) 485–491
smaller c3
larger c2
Buffer cost
smaller c2
k*=3
k*=3
look-ahead
look-back (larger cost)
Reset cost
(smaller cost)
k*=1 or 2
look-back (smaller cost) larger c3
491
look-ahead (larger cost)
k*=0
k*=0
Fig. 11. Look-ahead/back strategy: case of T ¼ 5.
demand-to-supply and process-cycle management models toward enterprise modeling and integration (Matsui, 1999). In the near future, these stochastic models will be formularized and designed for a stochastic/strategic management. In addition, this study would develop enterprise/factory science and management (Binder and Clegg, 2007), combined with a ERP/SCM balancing issue. References Abe, K., Yamada, T., Matsui, M., 2007. A design approach to stochastic mixed-line with look-ahead. The Japan Society of Logistics Systems 7 (2), 3–10. Ashby, W.R., 1956. An Introduction to Cybernetics. Wiley, New York. Binder, M., Clegg, B., 2007. Enterprise management: a new frontier for organizations. International Journal of Production Economics 106, 409–430. Buzacott, J.A., Shanthikumar, J.G., 1963. Stochastic Models of Manufacturing Systems. Prentice-Hall, New Jersey. Crabill, T.B., Cross, D., Manazine, M.J., 1977. A classified bibliography of research on optimal design and control of queues. Operations Research 25 (2), 219–232. Eliashberg, J., Steinberg, R., 1993. Marketing-production joint decisionmaking. In: Eliashberg, J., Lilien, G.L. (Eds.), Marketing. Elsevier, Amsterdam. Fayol, H., 1916. Administration Industrielle et Generale; General and Industrial Management. Pitman, New York. Follet, M.P., 1933. Freedom & Coordination—Lecture in Business Organisation. In: Urwick, L.H. (Ed.), Pitman, 1949. Hopp, W.J., Spearman, M.L., 2000. Factory Physics—Foundations of Manufacturing Management, second ed. McGraw-Hill, Boston, MA. Koontz, H., O’Donnell, C., 1959. Principles of Management–An Analysis of Managerial Functions. McGraw-Hill, New York. Matsui, M., 1993. A relationship of average criteria for queueing systems with lost units. In: Symposium on Performance Models for Information communication Networks, Shizuoka, Japan, pp. 371–378.
Matsui, M., 1999. An introduction to stochastic management and design, part I: management problem and structurization, vol. 43–44, part II: a general management problem and stochastic design, vol. 62–63, Preprints of Japan Industrial Management Association, Autumn, Japan (in Japanese). Matsui, M., 2002. A management game model, economic traffic, leadtime and pricing setting. Journal of Japan Industrial Management Association 53 (1), 1–9. Matsui, M., 2005. A management cycle model: switching control under lot processing and time span. Journal of Japan Industrial Management Association 56 (4), 256–264. Matsui, M., 2006. Management problem of strategic MRP II/ERP/ SCM—Sei-Han collaboration and pair-strategic map. Management System 16 (1), 43–46 (in Japanese). Matsui, M., Nakada, Y., 2003. Strategic management/design approach to a job-shop production system. International Journal of Production Research 41 (14), 3257–3271. Matsui, M., Wang, L., 2007. An economic/lead-time design comparison and map for central server systems including FMS. International Journal of Automation Technology 1 (1), 35–44. Reid, R.A., Cseko, G.C., 2006. Support service process improvement: an application within a university health sciences center. International Journal of Productivity and Quality Management 1 (4), 339–362. Schewhart, W.A., 1939. Statistical method from the viewpoint of quality control. The Graduate School, US Department of Aqriculture, Washington. Shannon, C.E., Weaver, W., 1964. The Mathematical Theory of Communication. The University of Illinois Press, Urbana, IL. Shapiro, B.P., 1977. Can marketing and manufacturing coexist? Harvard Business Review (September–October). Takahashi, T., 1999. Fundamental concept of quality control and SQC/ TQM. Management System 9 (1), 60–63 (in Japanese). Taylor, F.W., 1903. Shop management. In: Scientific Management. Harper & Brothers, NY, 1947. Yamada, T., Kakefuta, S., Matsui, M., 2006. Strategic selection of assembly systems under viable demands. Assembly Automation 26 (4), 335–342. Yamato, J., Yamada, T., Matsui, M., Nof, S.Y., 2003. A cooperation/ collaboration logic by sharing information in an ERP model. In: Proceedings of ICPR17, Blacksburg, VA (on CD-ROM).
Contents lists available at ScienceDirect
Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe
Enterprise modeling and integration: A stochastic management approach Masayuki Matsui Department of Systems Engineering, The University of Electro-Communications, Chofu-Shi, Tokyo 182-8585 Japan
a r t i c l e in fo
abstract
Available online 21 June 2009
Recently, my book by Matsui has been published in Japanese, and the title is ‘‘Management of Manufacturing Enterprise: Profit Maximization and Factory Science.’’ Enterprise/Factory Science means the system science of the 3M & I, in which is the complex of huMan, Material/Machine, Money and Information, and would contribute to enterprise modeling and integration issues. This paper presents the problem of enterprise/factory science, and discusses a class of 3M & I system by a stochastic management approach. First, the 3M & I system is defined, and a variety vs. structure is introduced. Next, the two structures of management are distinguished, and the demand-to-supply and process-cycle management are discussed by structural modeling. & 2009 Elsevier B.V. All rights reserved.
Keywords: 3M & I system Enterprise modeling Demand-to-supply Process cycle Ellipse/switching
1. Introduction Taylor, the father of scientific management, said in 1903 that management is an art (Taylor, 1903). By ‘‘an art’’ he meant the scientific law/technique (enterprise/factory) and the applicative ‘‘waza’’ (in Japanese, skill/know-how). Following that, this definition was further developed by Koontz and O’Donnell (1959) and others. About 100 years of the history of management has passed since Taylor established a theory of scientific management (Taylor, 1903). However, management theory regarding what management actually still seems to be confusing (Koontz and O’Donnell, 1959) and at a static/ statistical level when referring to history of management. The statistical approach was effective in the era of mass production since scientific management. However, this approach would be limited to the class of average management and not proper to develop the micro I systematic management in the era of individual. This chapter is preparation for developing management theories through modeling experiments by a
E-mail address: [email protected] 0925-5273/$ - see front matter & 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2009.06.023
stochastic approach. Towards this purpose, we have developed a system approach to management modeling, and attempt to build a framework for the management research in the future.
2. Stochastic management 2.1. 3M & I system As management is always facing environmental changes, it is natural to consider stochastic management by stochastic approaches (Buzacott and Shanthikumar, 1963; Crabill et al., 1977; Hopp and Spearman, 2000; Matsui, 1993). In stochastic management, the structure design theory is important for dispersing risk and for buffer design. At the beginning, we present general management problems for a variety of 3M & I system. Next, we consider the structure design problem for the general management problem and present a new or unified approach for the art of variety. The system is usually defined as the 3M & I system, consisting of huMan, Material/Machine, Money and
ARTICLE IN PRESS 486
M. Matsui / Int. J. Production Economics 122 (2009) 485–491
Information as shown in Fig. 1. The use of 3M represents resources, while I represent methods. The possible states of the 3M & I system are usually numerous, and the state space of variety is our main concern. Then, the management is here regarded as the management of the 3M & I system as shown in Fig. 2. The 3M & I manufacturing/enterprise is further divided into project and repetitive types, and the latter type is our object system and is treated below. Without the loss of generality, the 3M system is assumed to vary stochastically in time processes under risks.
Environment Material Machine
Man
&
Money
Information
Fig. 1. 3M & I system.
(man nnel
3. Demand-to-supply management
)
Perso
ney
(mo
Manufacture (mertial/machines)
The management is now defined as an art of variety in a 3M & I system under the goal, and is called the general management problem. Individualization and uniformity are the opposite extremes of variety, and usual management (standardization) occurs between the extremes. There is a law of requisite variety (Ashby, 1956). If the redundancy (Shannon and Weaver, 1964) in information theory is introduced as the measure of management constraints, the degree of management standardization/ structure can be shown. Recent globalization in the world is moving towards standardization, while manufacturing culture is moving toward individualization. Since there are various kinds of management which are diversified, management also has a redundancy of structures for standardizations. According to past literature, the management process and demand-to-supply structures would be the two main types of management structures. Each structure plays a vital role in preventing variety through the I (information/method) from disturbance. In our examination, we focus on the structures as equilibrium system, and a significant amount of work on management problem is created through stochastic modeling. Since Taylor published ‘‘Shop Management’’ in 1903, about 100 years have passed. With regard to environment/ object, variety and structure, the difference between Taylor’s ideas and today’s ideas are arranged in Table 1, as well as the flow of management principle.
nce
Method
Environment (market, life, nature, etc)
Fina
)
Enterprise
2.2. Variety and structure
3.1. 2-center problem Information/ Quality Marketing (product)
Management
Fig. 2. 3M & I and enterprise.
Recently, there is an increased emphasis on the integration of the separate functional area of the firm. This phenomenon has been reflected in a number of recent textbooks addressing the integration issues between marketing and production management (Eliashberg and Steinberg, 1993). This problem was first pointed out by Follet et al. (1933) (Follet, 1933; (Shapiro, 1977), and it involves a class of heterogeneous agents.
Table 1 100 years since Taylor. Since Taylor
Taylor
Today
Environment/ object
Market/production environment Object system
Economies of scale, division of labor Closed system
Economies of scope job enlargement Economies of agility work cooperation Open system Complex system
Variety
Standardization
Work standardization
Individualization
Human machinery
Standardization of production and quality Human relations job design
Management process
Separation of plan and administration Functionalization (plan)
General management control/ decision CIM flexible
Structure
Demand-to-supply
Globalization ISO standard Multi-agent manufacturing culture Management strategy PDCA/ CAPD ERP/SCM agile/visible
ARTICLE IN PRESS M. Matsui / Int. J. Production Economics 122 (2009) 485–491
487
Table 2 Four types of two centers models. Types
Patterns
Management
Domination
TOC/divisionalization
Sales
Production or Sales
Production
Trade/distributed (non-cooperation)
Compromise
Sales
Production
Integration
Collaboration/centralized (cooperation)
Sales
Production
Sharing
VMI/remote
Sales
Production
Sales
Production
Market risks Customers
Sales (Service) Center
Operational risks Buffer
Production Delivery Center
Lost Customers (Latent demand) Fig. 3. 2-center model.
Now, consider the 2-center model in which enterprises use for sales and production centers (Fig. 3) (Matsui, 1999). The sales center would pursue the maximization of the demand price, while the production center would pursue the minimization of operating cost. For the problem of the two centers, the goal of the factory/enterprise is to maximize the difference between reward and cost under the shorter leadtime by the collaboration/cooperation of the two centers. There are the four types of 2-center models in integration (Table 2). We focus on the stochastic modeling and design for the demand-to-supply management type, and we present a framework theory for management design on the basis of 2-center models (Matsui, 1993, 2006). A general framework for demand-to-supply management model is seen in Fig. 4 and is called the management
game model (MGM) (Matsui, 2002). Based on this framework, some factory systems are distinguished such as stochastic systems as lot/cell production, conveyor systems, job-shop production, flexible manufacturing and so on. 3.2. Toward 3-center In the 3-center model (Yamato et al., 2003), there is a triple relation of 32( ¼ 9) ways. Two major types are centralized and distributed (Fig. 5), and are compared in Table 3 (Matsui, 2006). This difference is similar to that of the enterprise resource planning (ERP) vs. supply chain management (SCM) type, and these would be contrasted to balancing issues. 4. Process-cycle management 4.1. Two problems of cycles The management process is characterized by a management cycle approach, in which functions of management are roughly divided into plan, organization, direction and control (Fayol, 1916). Also, the management cycle is well known as a model of management process structure in industry.
ARTICLE IN PRESS 488
M. Matsui / Int. J. Production Economics 122 (2009) 485–491
Strategic/Gaming Coordination
Sales Center (Pricing Setting/ Order Selection)
Customer/Job
Production Center
Buffer
Delivery
(Cycle/cell, Conveyor, Job-shop, Flexible etc.)
Lost/Subcontract Leadtime Fig. 4. General framework for demand-to-supply.
Sales
Plan(P) Plan(P)
Do(D) Do(D)
Check(C) Check(C)
Check(C) Check(C)
Act(A) Act(A)
Plan(P) Plan(P)
Act(A) Act(A)
S S Sales PP Production
M M
Manufacturing
S S
PP
M M
Do(D) Do(D)
Production
Manufacturing
Fig. 5. Typical types of 3-center: (a) centralized type and (b) distributed type.
Fig. 6. Two cycle model: (a) PDCA type and (b) CAPD type.
Table 3 3-center problem: centralized vs. distributed.
X
Comparison
Centralized
Distributed
Relation Division of work Package Module Goal
Star Make ERP Vertical Common
Series Buy SCM Horizontal Individual
transform
material
X
transform
As a typical case, the PDCA, Plan-Do-Check-Act, is commonly used in quality control etc. at the factory (Reid and Cseko, 2006; Takahashi, 1999). It may be distinguished as the PDCA cycle, starting from plan, and the CAPD cycle, starting from check, in Fig. 6. For example, the plan corresponds to the setting of the control limit level. The original version of PDCA is seen in the PDS, which consists of Plan (specification/hypothesis), Do (production
Y
Fig. 7. PDCA vs. CAPD: (a) PDCA (feedforward) and (b) CAPD (feedback).
/experiment) and See (inspection/judgment) in statistical quality control (Schewort, 1939). Recently, the CAPD type has been issued.
ARTICLE IN PRESS M. Matsui / Int. J. Production Economics 122 (2009) 485–491
489
Table 4 Classification of management processes. Cycle type
PDCA
CAPD
Type
Design approach Open approach Future planning Goal seeking Top-down
Improvement approach Closed approach Ex post facto action Problem solving Bottom-up
Object
Instruction form Control form Trigger form Retail form Medical-care form
Formal system Feedforward Push system ‘‘goyoukiki’’ Human dock
Informal system Feedback Pull system Supermarket Doctor activity
Technique
Process control Quality control Inventory control Production control Bottleneck control Data control Cost control
Gantt chart (forward) Control chart EOQ MRP MGM (Matsui, 1999) Data mining Standard cost system
Backward Bayesian chart Double-bin JIT TOC/MGM Data analysis Actual cost system
Management
Approach
4.2. PDCA vs. CAPD A comparison of PDCA and CAPD is seen in Fig. 7. The material and information flows are the same as those shown in Fig. 7(a), while these are dual flows as shown in Fig. 7(b). Generally, the difference would be smaller under larger due-time, because the influence of the starting point would be diminished. Also, alternatives of PDCA vs. CAPD are classified in Table 4. An additional feature of PDCA vs. CAPD would be seen in the shorter process cycles with due date barrier, and is treated stochastically as a management cycle model (MCM) with switching (Matsui, 2005).
m Production speed
Demand speed
d)
(ER, EC) Leadtime
Fig. 8. Pair-matrix concept. ER: revenue and EC: operating cost.
tion and minimization. The cross section is the zone of profit maximization. 5. Application case 5.1. Job-shop case The application case of enterprise modeling and integration is the job-shop case of order-selection and switchover, and the lot production case of two service rates and time span. The former is the game-modeling type of two centers, and the latter is the PDCA-modeling type of lot processing. A gaming situation of two centers is treated by the introduction of pair-matrix concept. Fig. 8 illustrates the pair-matrix concept, and Fig. 9 is an application to the enterprise of job-shop type (Matsui and Nakada, 2003). Examples of other types are seen in (Abe et al., 2007; Matsui and Wang, 2007; Yamada et al., 2006). There are the ellipse-cross charts on pair-matrix table. The ellipse-cross chart consists of the ellipses of economics and reliability. The former has the pair poles of revenue maximization (ER*) and cost minimization (EC*). The latter has the pair poles of leadtime maximiza-
5.2. Lot production case A switching situation of PDCA cycle is treated by the introduction of two service rates in lot production. Fig. 10 illustrates the two service rates and penalties, and Fig. 11 is an applied result to the enterprise of lot processing (Matsui, 2005). These are the switching-cross chart on the earliness and tardiness table. The switching-cross chart shows the map of pair (t, k), in which t and k is the processed time and number at switching point. On the lower cost criterion (ECp), there is the no-switching zone in the four corners, and is the switching zone in the central section. 6. Conclusion and future In this paper, the enterprise/factory is regarded as a 3M & I system, and is modeled by a not statistical but stochastic management approach. These include the
ARTICLE IN PRESS 490
M. Matsui / Int. J. Production Economics 122 (2009) 485–491
Maximal revenue
Maximal Leadtime
Maximal profit
Minimal leadtime
Minimal cost
Fig. 9. Pair-matrix table and ellipse (job-shop type). (c1, c2): order-selection criterion (i1, i2): number of backlog (state).
look-ahead cycle
k’ (Switch)
k (Switch)
Workload
look-back cycle
Earliness
Tardiness 2
2
C2
0
1
1
t’
t
C3
T (due time)
Fig. 10. Two service rates and penalties. m1: slower speed and m2: faster speed.
ARTICLE IN PRESS M. Matsui / Int. J. Production Economics 122 (2009) 485–491
smaller c3
larger c2
Buffer cost
smaller c2
k*=3
k*=3
look-ahead
look-back (larger cost)
Reset cost
(smaller cost)
k*=1 or 2
look-back (smaller cost) larger c3
491
look-ahead (larger cost)
k*=0
k*=0
Fig. 11. Look-ahead/back strategy: case of T ¼ 5.
demand-to-supply and process-cycle management models toward enterprise modeling and integration (Matsui, 1999). In the near future, these stochastic models will be formularized and designed for a stochastic/strategic management. In addition, this study would develop enterprise/factory science and management (Binder and Clegg, 2007), combined with a ERP/SCM balancing issue. References Abe, K., Yamada, T., Matsui, M., 2007. A design approach to stochastic mixed-line with look-ahead. The Japan Society of Logistics Systems 7 (2), 3–10. Ashby, W.R., 1956. An Introduction to Cybernetics. Wiley, New York. Binder, M., Clegg, B., 2007. Enterprise management: a new frontier for organizations. International Journal of Production Economics 106, 409–430. Buzacott, J.A., Shanthikumar, J.G., 1963. Stochastic Models of Manufacturing Systems. Prentice-Hall, New Jersey. Crabill, T.B., Cross, D., Manazine, M.J., 1977. A classified bibliography of research on optimal design and control of queues. Operations Research 25 (2), 219–232. Eliashberg, J., Steinberg, R., 1993. Marketing-production joint decisionmaking. In: Eliashberg, J., Lilien, G.L. (Eds.), Marketing. Elsevier, Amsterdam. Fayol, H., 1916. Administration Industrielle et Generale; General and Industrial Management. Pitman, New York. Follet, M.P., 1933. Freedom & Coordination—Lecture in Business Organisation. In: Urwick, L.H. (Ed.), Pitman, 1949. Hopp, W.J., Spearman, M.L., 2000. Factory Physics—Foundations of Manufacturing Management, second ed. McGraw-Hill, Boston, MA. Koontz, H., O’Donnell, C., 1959. Principles of Management–An Analysis of Managerial Functions. McGraw-Hill, New York. Matsui, M., 1993. A relationship of average criteria for queueing systems with lost units. In: Symposium on Performance Models for Information communication Networks, Shizuoka, Japan, pp. 371–378.
Matsui, M., 1999. An introduction to stochastic management and design, part I: management problem and structurization, vol. 43–44, part II: a general management problem and stochastic design, vol. 62–63, Preprints of Japan Industrial Management Association, Autumn, Japan (in Japanese). Matsui, M., 2002. A management game model, economic traffic, leadtime and pricing setting. Journal of Japan Industrial Management Association 53 (1), 1–9. Matsui, M., 2005. A management cycle model: switching control under lot processing and time span. Journal of Japan Industrial Management Association 56 (4), 256–264. Matsui, M., 2006. Management problem of strategic MRP II/ERP/ SCM—Sei-Han collaboration and pair-strategic map. Management System 16 (1), 43–46 (in Japanese). Matsui, M., Nakada, Y., 2003. Strategic management/design approach to a job-shop production system. International Journal of Production Research 41 (14), 3257–3271. Matsui, M., Wang, L., 2007. An economic/lead-time design comparison and map for central server systems including FMS. International Journal of Automation Technology 1 (1), 35–44. Reid, R.A., Cseko, G.C., 2006. Support service process improvement: an application within a university health sciences center. International Journal of Productivity and Quality Management 1 (4), 339–362. Schewhart, W.A., 1939. Statistical method from the viewpoint of quality control. The Graduate School, US Department of Aqriculture, Washington. Shannon, C.E., Weaver, W., 1964. The Mathematical Theory of Communication. The University of Illinois Press, Urbana, IL. Shapiro, B.P., 1977. Can marketing and manufacturing coexist? Harvard Business Review (September–October). Takahashi, T., 1999. Fundamental concept of quality control and SQC/ TQM. Management System 9 (1), 60–63 (in Japanese). Taylor, F.W., 1903. Shop management. In: Scientific Management. Harper & Brothers, NY, 1947. Yamada, T., Kakefuta, S., Matsui, M., 2006. Strategic selection of assembly systems under viable demands. Assembly Automation 26 (4), 335–342. Yamato, J., Yamada, T., Matsui, M., Nof, S.Y., 2003. A cooperation/ collaboration logic by sharing information in an ERP model. In: Proceedings of ICPR17, Blacksburg, VA (on CD-ROM).